Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: supercomputing – Page 35

IBM debuts next-gen quantum processor and IBM quantum system two, extends roadmap to advance quantum utility

At the annual IBM Quantum Summit in New York, IBM debuted IBM Quantum Heron, the first in a new series of utility-scale quantum processors with an architecture engineered over the past four years to deliver IBM’s highest performance metrics and lowest error rates of any IBM Quantum processor to date.

IBM also unveiled IBM Quantum System Two, the company’s first modular quantum computer and cornerstone of IBM’s quantum-centric supercomputing architecture. The first IBM Quantum System Two, located in Yorktown Heights, New York, has begun operations with three IBM Heron processors and supporting control electronics.

With this critical foundation now in place, along with other breakthroughs in quantum hardware, theory, and software, the company is extending its IBM Quantum Development Roadmap to 2033 with new targets to significantly advance the quality of gate operations. Doing so would increase the size of quantum circuits able to be run and help to realize the full potential of quantum computing at scale.

A New Brain-Like Supercomputer Aims to Match the Scale of the Human Brain

A supercomputer scheduled to go online in April 2024 will rival the estimated rate of operations in the human brain, according to researchers in Australia. The machine, called DeepSouth, is capable of performing 228 trillion operations per second.

It’s the world’s first supercomputer capable of simulating networks of neurons and synapses (key biological structures that make up our nervous system) at the scale of the human brain.

DeepSouth belongs to an approach known as neuromorphic computing, which aims to mimic the biological processes of the human brain. It will be run from the International Center for Neuromorphic Systems at Western Sydney University.

A new supercomputer aims to closely mimic the human brain — it could help unlock the secrets of the mind and advance AI

Neuromorphic computers are based on intricate networks of simple, elementary processors (which act like the brain’s neurons and synapses). The main advantage of this is that these machines are inherently “parallel”.

This means that, as with neurons and synapses, virtually all the processors in a computer can potentially be operating simultaneously, communicating in tandem.

In addition, because the computations performed by individual neurons and synapses are very simple compared with traditional computers, the energy consumption is orders of magnitude smaller. Although neurons are sometimes thought of as processing units, and synapses as memory units, they contribute to both processing and storage. In other words, data is already located where the computation requires it.

IBM demonstrates useful Quantum computing within 133-qubit Heron, announces entry into Quantum-centric supercomputing era

At its Quantum Summit 2023, IBM took the stage with an interesting spirit: one of almost awe at having things go their way. But the quantum of today – the one that’s changing IBM’s roadmap so deeply on the back of breakthroughs upon breakthroughs – was hard enough to consolidate. As IBM sees it, the future of quantum computing will hardly be more permissive, and further improvements to the cutting-edge devices it announced at the event, the 133-qubit Heron Quantum Processing Unit (QPU), which is the company’s first utility-scale quantum processor, and the self-contained Quantum System Two, a quantum-specific supercomputing architecture, are ultimately required.

But each breakthrough that afterward becomes obsolete is another accelerational bump against what we might call quantum’s “plateau of understanding.” We’ve already been through this plateau with semiconductors, so much so that our latest CPUs and GPUs are reaching practical, fundamental design limits where quantum effects start ruining our math. Conquering the plateau means that utility and understanding are now enough for research and development to be somewhat self-sustainable – at least for a Moore’s-law-esque while.

World’s First Human ‘Brain-Scale’ Supercomputer Will Go Online in 2024

Our brains are remarkably energy efficient.

Using just 20 watts of power, the human brain is capable of processing the equivalent of an exaflop — or a billion-billion mathematical operations per second.

Now, researchers in Australia are building what will be the world’s first supercomputer that can simulate networks at this scale.

World’s first human brain-scale neuromorphic supercomputer is coming

ICYMI: DeepSouth uses a #neuromorphiccomputing system which mimics biological processes, using hardware to efficiently emulate large networks of spiking #neurons at 228 trillion #Synaptic operations per second — rivalling the estimated rate of operations in the human brain.

Australian researchers are putting together a supercomputer designed to emulate the world’s most efficient learning machine – a neuromorphic monster capable of the same estimated 228 trillion synaptic operations per second that human brains handle.

As the age of AI dawns upon us, it’s clear that this wild technological leap is one of the most significant in the planet’s history, and will very soon be deeply embedded in every part of our lives. But it all relies on absolutely gargantuan amounts of computing power. Indeed, on current trends, the AI servers NVIDIA sells alone will likely be consuming more energy annually than many small countries. In a world desperately trying to decarbonize, that kind of energy load is a massive drag.

But as often happens, nature has already solved this problem. Our own necktop computers are still the state of the art, capable of learning super quickly from small amounts of messy, noisy data, or processing the equivalent of a billion billion mathematical operations every second – while consuming a paltry 20 watts of energy.

Gravitational Waves Unveil Thermal Secrets in Neutron Star Mergers

Simulations of binary neutron star mergers suggest that future detectors will distinguish between different models of hot nuclear matter.

Researchers used supercomputer simulations to explore how neutron star mergers affect gravitational waves, finding a key relationship with the remnant’s temperature. This study aids future advancements in detecting and understanding hot nuclear matter.

Exploring neutron star mergers and gravitational waves.

A Ball of Brain Cells on a Chip Can Learn Simple Speech Recognition and Math

The mini-brain functioned like both the central processing unit and memory storage of a supercomputer. It received input in the form of electrical zaps and outputted its calculations through neural activity, which was subsequently decoded by an AI tool.

When trained on soundbites from a pool of people—transformed into electrical zaps—Brainoware eventually learned to pick out the “sounds” of specific people. In another test, the system successfully tackled a complex math problem that’s challenging for AI.

The system’s ability to learn stemmed from changes to neural network connections in the mini-brain—which is similar to how our brains learn every day. Although just a first step, Brainoware paves the way for increasingly sophisticated hybrid biocomputers that could lower energy costs and speed up computation.

/* */